Systems and methods for predicting characteristics of an artificial heart using an artificial neural network

ABSTRACT

A system configured to predict characteristics of an artificial heart is described. The system includes a processor and memory in electronic communication with the processor, and an artificial neural network configured to receive an input vector of a predetermined length to train the artificial neural network, produce an output vector based on the input vector, and compare the output vector with a target vector of the predetermined length. When the output vector does not match the target vector within a predetermined error rate, the network is configured to adjust at least one weight, and when the output vector matches the target vector within the predetermined error rate, the network is configured to execute the input vector to produce an estimate at least one characteristic of the artificial heart.

BACKGROUND

Artificial heart pumps may be classified into the reciprocating type,the rotary displacement type, the centrifugal type, and the turbo typethat operates by rotational flow. Artificial heart pumps of thecentrifugal type may be equipped with a casing, a rotor disposed insidethe casing, a motor for rotating the rotor, a blood flow channel forintroducing and guiding the flow of blood, and an impeller that rotatesintegrally with the rotor for imparting centrifugal force to the bloodflowing in through the blood flow channel formed in the casing.

Centrifugal type artificial hearts conventionally use ball bearings forrotatably supporting the rotor coupled with the impeller. In suchconfigurations, blood flow is liable to stagnate in the vicinity of theball bearing. An artificial heart pump that is susceptible to suchstagnation may pose significant problems. The formation of stagnantblood is known to be a primary cause of blood coagulation. To eliminatethis drawback, artificial heart pumps that include rotors suspended in anon-contacting state by magnetic forces have been used. Thesemagnetically levitated heart pumps may constantly maintain the rotor inthe proper attitude by regulating the current supplied to theelectromagnets so as to control their magnetic field.

Prior efforts at developing a flow estimation method for artificialhearts, in an attempt to properly regulate the current supplied to theelectromagnets, have focused on developing a linear equation describingthe physics of the system, then using least-squared methods to find abest fit for the coefficients for the equation. These prior efforts,however, do not provide accurate estimates for magnetically levitatedheart pumps because any dynamic force across the pump may perturb theposition of the levitated rotor, which may change properties of thelevitated pump. As a result, benefits may be realized by providingsystems and methods for predicting characteristics of an artificialheart using an artificial neural network.

SUMMARY

According to at least one embodiment, a system configured to predictcharacteristics of an artificial heart is described. The system includesa processor and memory in electronic communication with the processor,and an artificial neural network configured to receive an input vectorof a predetermined length to train the artificial neural network,produce an output vector based on the input vector, and compare theoutput vector with a target vector of the predetermined length. When theoutput vector does not match the target vector within a predeterminederror rate, the network is configured to adjust at least one weight, andwhen the output vector matches the target vector within thepredetermined error rate, the network is configured to execute the inputvector to produce an estimate at least one characteristic of theartificial heart.

In one embodiment, the artificial heart is an artificial heart having atleast one magnetically levitated component. In one example, the at leastone characteristic of the artificial heart may be positive fluid flowproduced by the artificial heart. In another example, the at least onecharacteristic of the artificial heart may be negative fluid flowproduced by the artificial heart. The at least one characteristic of theartificial heart may also be differential pressure exhibited across aninlet and an outlet of the artificial heart. The at least onecharacteristic of the artificial heart may also be an pulsitility index.In one embodiment, the artificial neural network may predict a conditionof a patient that uses the artificial heart.

In one configuration, the input vector may include at least one of rotorspeed, rotor position, or motor current of the artificial heart. Theinput vector may also include at least one of levitation current, systemcurrent, or system voltage. In one embodiment, the artificial heart maybe a left ventricular assist device (LVAD). In one example, theartificial heart may be a right ventricular assist device (RVAD).

A computer-implemented method to predict characteristics of anartificial heart using an artificial neural network is also described.An input vector of a predetermined length may be received to train theartificial neural network. An output vector may be produced based on theinput vector. The output vector may be compared with a target vector ofthe predetermined length. When the output vector does not match thetarget vector within a predetermined error rate, at least one weight ofthe artificial neural network may be adjusted. When the output vectormatches the target vector within the predetermined error rate, theartificial neural network may execute the input vector to produce anestimate at least one characteristic of the artificial heart.

A computer-program product for predicting characteristics of anartificial heart using an artificial neural network is also described.The computer-program product may include a non-transitorycomputer-readable medium having instructions thereon. The instructionsmay include code programmed to receive an input vector of apredetermined length to train the artificial neural network, and codeprogrammed to produce an output vector based on the input vector. Theinstructions may further include code programmed to compare the outputvector with a target vector of the predetermined length. When the outputvector does not match the target vector within a predetermined errorrate, the instructions may include code programmed to adjust at leastone weight. When the output vector matches the target vector within thepredetermined error rate, the instructions may include code programmedto execute the input vector to produce an estimate at least onecharacteristic of the artificial heart.

A system configured to predict conditions of a patient using anartificial heart is also described. The system may include a processorand memory in electronic communication with the processor. The systemmay further include an artificial neural network configured to receivean input vector of a predetermined length to train the artificial neuralnetwork, and produce an output vector based on the input vector. Theartificial neural network may be further configured to compare theoutput vector with a target vector of the predetermined length. When theoutput vector does not match the target vector within a predeterminederror rate, the artificial neural network may be configured to adjust atleast one weight. When the output vector matches the target vectorwithin the predetermined error rate, the artificial neural network maybe configured to execute the input vector to produce an estimate of atleast one condition of the patient using the artificial heart.

In one configuration, the patient may use a natural heart in conjunctionwith the artificial heart. The at least one condition of the patient maybe hematocrit properties of the patient. In one example, the at leastone condition of the patient may be a pulsitility index of the blood ofthe patient. The pulsitility index may describe the strength of anatural heart of the patient being used in conjunction with theartificial heart.

In one configuration, the at least one condition of the patient may beblood viscosity properties of the patient. The at least one condition ofthe patient may be a recovery measurement of the natural heart of thepatient. In one embodiment, the recovery measurement may be ameasurement of the contractility of a native ventricle of the naturalheart of the patient. In one example, the recovery measurement may be ameasurement of the elastance of a native ventricle of the natural heartof the patient.

Features from any of the above-mentioned embodiments may be used incombination with one another in accordance with the general principlesdescribed herein. These and other embodiments, features, and advantageswill be more fully understood upon reading the following detaileddescription in conjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate a number of exemplary embodimentsand are a part of the specification. Together with the followingdescription, these drawings demonstrate and explain various principlesof the instant disclosure.

FIG. 1 is a block diagram illustrating one embodiment of an environmentin which the present systems and methods may be implemented;

FIG. 2 is a block diagram illustrating one embodiment of an artificialneural network that may be implemented in the present systems andmethods;

FIG. 3 is a block diagram illustrating multiple transfer functions inaccordance with the present systems and methods;

FIG. 4 is a screen shot of a user interface illustrating one embodimentof a training tool interface for the artificial neural network;

FIG. 5 is a flow diagram illustrating one embodiment of a method topredict characteristics of an artificial heart;

FIG. 6 is a flow diagram illustrating one embodiment of a method topredict conditions of a patient who has an artificial heart;

FIG. 7 depicts a block diagram of a computer system suitable forimplementing the present systems and methods; and

FIG. 8 is a block diagram depicting a network architecture in whichclient systems, as well as storage servers (any of which can beimplemented using computer system), are coupled to a network.

While the embodiments described herein are susceptible to variousmodifications and alternative forms, specific embodiments have beenshown by way of example in the drawings and will be described in detailherein. However, the exemplary embodiments described herein are notintended to be limited to the particular forms disclosed. Rather, theinstant disclosure covers all modifications, equivalents, andalternatives falling within the scope of the appended claims.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

An artificial heart is a mechanical device that may replace the heart ofa human or other mammal. An artificial heart may be used to bridge thetime to heart transplantation, or to permanently replace the heart inthe case that transplantation is not possible. Although the heart isconceptually simple (basically a muscle that functions as a pump), theheart embodies subtleties that may defy straightforward emulation withsynthetic materials and power supplies. Consequences of these issues mayinclude severe foreign-body rejection and external power sources thatlimit patient mobility.

Further, the development of artificial hearts may require the developersto accurately estimate certain characteristics of the artificial heart.For example, developers may desire to accurately estimate flow,differential pressure, and hematocrit properties of an artificial heart.In addition, developers may desire to accurately predict the orientation(e.g., standing up, lying down, inclined, etc.) of a patient that isusing an artificial heart.

A ventricular assist device (VAD) is a mechanical circulatory devicethat may be used to partially or completely replace the function of afailing heart. A VAD may be used for a short time period (e.g., during arecovery period following a heart attack or heart surgery). A VAD may beused for a long time period (e.g., for patients suffering fromcongestive heart failure. In one embodiment, VADs may be designed toassist either the right (RVAD) or left (LVAD) ventricle. Long term VADsmay be used to keep patients alive with an acceptable quality of lifewhile they wait for heart transplantation. LVADs, however, may be usedas destination therapy and sometimes as a bridge to recovery.

The term artificial heart, as used herein, may refer to an artificialheart device that typically takes over the cardiac function of a heart.The term artificial heart, as used herein, may also refer to VADs, forexample, the RVAD and the LVAD.

Prior efforts at developing flow estimation for artificial hearts havefocused on developing a linear equation that may describe the physics ofthe system (i.e., patient and artificial heart). These previous effortsmay then use a least-square method to find a best fit for thecoefficients for the developed linear equation. As a result, priorefforts have focused on the “system identification” problem, where it isdesired to develop a model that describes (predicts) the function of adevice. For non-trivial systems with non-linear properties and unknowncoupling mechanisms, developing a direct equation may be difficult,employing trial and error methods. A non-linear neural network is knownto find the underlying model, regardless of the nonlinearity, couplingmechanisms given sufficient “Shannon” information in the input space,along with an adequate topology for the neural network.

A magnetically levitated artificial heart may include a rotor andimpeller that are suspended with no support other than magnetic fields.These components may be suspended by constantly altering the strength ofa magnetic field produced by electromagnets. Magnetic levitation mayreduce the energy consumption of the artificial heart. In addition, theenergy efficiency of the artificial heart may be increased by minimizingthe contact points between frictional surfaces. Flow estimationprocesses used in prior efforts, however, may not provide adequateestimations when used with an artificial heart employing magneticlevitation. Dynamic forces across the artificial heart (e.g., the leftventricle), may perturb the position of the levitated rotor. This maychange the magnetic coupling, the volume of the pumping chamber, and theresulting shear forces on blood. In one configuration, magneticallylevitated components of an artificial heart may have no bearingsurfaces. This may provide many hemocompatibility benefits including,but not limited to, lower thrombus, stroke, and heart attack events.

In one embodiment, the present systems and methods may use an artificialneural network to determine a direct equation that describes amagnetically levitated artificial heart operating in a patient. In oneexample, artificial neural networks may include interconnectingartificial neurons (programming constructs that may emulate theproperties of biological neurons). In one configuration, artificialneural networks may either be used to gain an understanding ofbiological neural networks, or for solving artificial intelligenceproblems without necessarily creating a model of a real biologicalsystem. Artificial neural networks may be a tool to extract a directmodel of one or more unknown systems. Further, if an artificial neuralnetwork converges to some error tolerance, the input data may suffice toadequately and correctly describe such a system. The particularimplementation of an artificial neural network used by the presentsystems and methods may solve linear or nonlinear transforms from theinput data (such as vectors) to the prediction (estimation) space.

The present systems and methods may use artificial neural networkarchitecture to accurately estimate, for example, flow rates, pumpdifferential pressure, and hematocrit properties of a magneticallylevitated artificial heart. The artificial neural network may alsoaccurately predict the orientation of a patient that may have theartificial heart installed in his/her body. The algorithm resulting fromthe artificial neural network may predict both positive and negativeflow, pump differential, pressure, and viscosity (hematocrit) based onindirect signals controlling the artificial heart. Examples of inputs ofthese indirect signals may include, but is not limited to, rotor speed,motor current, levitation current, system current, and system voltage.

FIG. 1 is a block diagram illustrating one embodiment of an environmentin which the present systems and methods may be implemented. In oneexample, an artificial neural network 104 may receive at least one input102. The artificial neural network 104 may be used to determine anequation that describes a system, such as a magnetically levitatedheart. In one embodiment, the artificial neural network 104 may includeelements operating in parallel. The function of the network 104 may bedetermined by the connections (weights) between these elements. Theartificial neural network 104 may be trained to perform a particularfunction by adjusting the values of the connections (weights) betweenthe elements.

The artificial neural network 104 may generate an output 106 that may becompared to a target output 110. The output 106 and the target output110 may be compared by a comparing module 108. If the output 106 doesnot satisfy an error threshold when compared to the target output 110,adjusted weights 112 may be applied to the artificial neural network104. The adjusted weights 112 may adjust the output 106 generated by theartificial neural network 104. The artificial neural network 104 may beadjusted by the adjusted weights 112 until the output 106 satisfies theerror threshold when compared to the target output 110. As a result, theartificial neural network 104 may be adjusted (or trained) so that aparticular input 102 leads to a specific output 106. The network may beadjusted by the adjusted weights 112 based on a comparison of the output106 and the target output 110. The network 104 may be adjusted until theoutput 106 matches the target output 110.

In one configuration, the artificial neural network 104 may be batchtrained. Batch training may include providing adjusted weights 112 andbias changes based on an entire batch (set) of input vectors.Incremental training may adjust the weights and biases of the network104 after a presentation of each individual input vector 102.Incremental training may be referred to as “on line” or “adaptive”training.

As explained above, the artificial neural network 102 may be trained toperform complex functions in various fields, including, but not limitedto, pattern recognition, identification, classification, speech, vision,and control systems. The artificial neural network 102 may be trained tosolve problems that are difficult for conventional computers or humanbeings. Supervised training (as described above) may be used, but otherartificial neural networks may be obtained from unsupervised trainingtechniques or from direct design methods where training may not berequired. In one embodiment, unsupervised artificial neural networks maybe used, for example, to identify groups of data. Certain kinds ofnetworks may be designed directly. As a result, there are a variety ofdesigns and training techniques for developers of the artificial neuralnetwork 104.

FIG. 2 is a block diagram illustrating one embodiment of the artificialneural network 104 that may be implemented in the present systems andmethods. The network 104 may be trained to provide estimates for certaincharacteristics of an artificial heart. For example, the network 104 mayprovide estimates for flow rates, differential pressure, and hematocritproperties of the artificial heart. In addition, the network 104 maypredict the orientation of the patient that is using the artificialheart.

In one configuration, the network 104 may include an input layer 214, ahidden bias vector layer 216, a hidden layer 218, an output bias vectorlayer 220, and an output layer 222. In one configuration, an input dataset (vector) that includes a plurality of samples of input data may beinput 102 to the artificial neural network 104 (see FIG. 1) at the inputlayer 214. Input data sets at the input layer 214 may be summed with abias vector included in the hidden bias vector layer 216. In oneexample, a transfer function may be applied to the output from a biasvector in the hidden bias vector layer 216. An example of the transferfunction may be the following:

$\begin{matrix}{{\tan \; {{sig}(x)}} = \frac{2}{\left( {1 + ^{{- 2}x}} \right) - 1}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

FIG. 3 is a graph 300 illustrating one embodiment of multiple transferfunctions. In one configuration, the graph 300 includes a nonlineartransfer function 302, such as the tansig function listed as Equation 1above, and a linear transfer function 304.

The output from a neuron included in the hidden layer 218 may be summedwith a bias vector included in the output bias vector layer 220. In oneconfiguration, the output layer 222 may apply a pure linear function tothe output from the output bias layer 220. The output of the outputlayer 222 may include flow estimates, differential pressure estimates,and hematocrit estimates. An example equation representing the outputmay be:

$\begin{matrix}\left. {y = {{{{purelin}\left\lbrack \sum\limits_{k = 1}^{N} \right\rbrack}\left\lbrack {{\left\lbrack {\tan \; {{sig}\left\lbrack {\sum\limits_{k = 1}^{N}\left\lceil {\left( {I_{k} \cdot {Wa}_{k}} \right) + {ba}} \right\rceil} \right\rbrack}} \right\rbrack \cdot W}\; b_{k}} \right\rbrack} + {bb}}} \right\rbrack & {{Equation}\mspace{14mu} 2}\end{matrix}$

In Equation 2, “y” may be the output, “I” may be the input vector, “Wa”may be the hidden matrix, “Wb” may be the output matrix, “ba” may be thehidden bias vector, and “bb” may be the output bias vector. In oneembodiment, “tansig” may be the nonlinear operator that is anapproximation to “tan h”, defined as:

$\begin{matrix}{{\tanh \; x} = {\frac{\sinh \; x}{\cosh \; x} = {\frac{e^{x} - e^{- x}}{e^{x} + e^{- x}} = \frac{e^{2x} - 1}{e^{2x} + 1}}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In one embodiment, the network 104 may be trained, validated, and testedby providing an input data set including six columns each ofapproximately 81,000 samples of input data. A target vector of the samelength may be prepared with the corresponding flow and delta pumppressure data. In one configuration, flow training data may be providedby an ultrasonic flow measuring system. Pressure data may be providedfrom pressure transducers. Data available as inputs for training theartificial neural network 104 may include rotor rpm, rotor position,motor current, levitation current, system current, and system voltage ofan artificial heart. In one example, additional inputs may include(rotor speed)² (i.e., inertia), (rotor speed)*(rotor position), aderivative of rotor speed, a derivative of rotor position, and aderivative of levitation current of the artificial heart.

Using the above referenced inputs for training, the size of a matrix totrain the network 104 may be, but is not limited to, 11 columns×thenumber of training samples (approximately 81,000) samples. The trainingalgorithm may also use time information in the flow and pressureestimations. In one example, four additional time shifts may be used forthe input matrix in the algorithm. As a result, the size of the trainingmatrix may be 55 (5×11)×the 81,000 samples mentioned above. In oneembodiment, the target matrix may be two columns. A first column mayinclude the ultrasonic flow measurement for each input state. A secondcolumn may include the differential pressure measurements asinstrumented.

In one embodiment, the artificial neural network 104 may be created withapproximately (but not limited to) 55 neurons in the input layer 214(only 12 neurons shown for sake of simplicity and clarity) and threelinear output neurons (flow rate, differential pressure, and hematocritestimates). The input layer 214 may use the nonlinear tansig transferfunction provided above as Equation 1. The output layer 222 may be apure linear function. The network 104 may converge to solve linear ornonlinear functions between the inputs and outputs to some error boundedby the complexity of the network 104 (e.g., number of neurons). In oneconfiguration, training of the artificial neural network 104 may beperformed using the Levinburg Marquardt gradient descent method. Thismethod may provide a numerical solution to the problem of minimizing afunction, generally nonlinear, over a space of parameters of thefunction.

After the network 104 has been trained, it may be run on the entire dataset. In one configuration, the flow prediction error may be 0.0595liters/minute in the positive flow region and 0.1861 liters/minute inthe negative flow region. In one embodiment, the flow prediction in thenegative flow region may be affected by the ultrasonic flow measurementin the negative direction. In one embodiment, two back to back flowtransducers may be used to obtain training data for negative flowestimation. In addition, pressure data may be used to calculate flowestimation. Pressure data to calibrate transonic data may be used when abypass valve of the artificial heart is open on the loop.

In one configuration, data on a particular known pump may be gatheredwhere hematocrit was known. The network 104 may include a third outputfor hematocrit predictions. In order to estimate hematocrit, the numberof columns of the matrix described above may be increased to 65×65. Withthe larger matrix, the errors for the predictions of flow, differentialpressure, and hematocrit may be close to zero.

In one example, an input vector may be provided as the input to thenetwork. In one embodiment, the input vector may include 65 inputneurons. The input layer may include control information, derivatives,squares, etc. as well as the time shifted version of the indirect data.The hidden layer may also include 65 neurons. The output of the hiddenlayer may be formed by a nonlinear operator over the sum of the weightmatrix plus a bias vector. The weights and biases may be solved througha gradient descent method, such as the Levinburg Marquardt gradientdescent method. The artificial neural network of the present systems andmethods may include at least three output neurons with a linear outputoperator (e.g., pure linear function). The three output neurons (one foreach of the predicted functions) may be formed by a linear operator overthe sum of inputs (from the hidden layer), and a bias. The weights andbiases may be solved simultaneously with the hidden layer weights andbiases using the gradient descent method described above.

In one embodiment, the artificial neural network 104 of the presentsystems and methods may include an input vector of 65 neurons and ahidden layer of 65 neurons. The inner layer may be solved in 65 multiplyadds followed by 65 nonlinear tansig( ) operations. For each of thethree output neurons, the output may be solved with a 65×1 multiply addfollowed by a purelin( ) transform.

In one configuration, the artificial neural network 104 of the presentsystems and methods may be trained by presenting input vectors andtarget vectors, taking the error between the two, and using an efficientgradient descent method (e.g., Levinburg Marquardt) that may result intwo internal matrices converging such that the input space may match thetarget vector space under all operating conditions. The resulting innermatrix (from the convergence of the two internal matrices) may be 65×65.

FIG. 4 is a screen shot of a user interface illustrating one embodimentof a training tool interface 400 for the artificial neural network 104.The tool 400 may provide a simplified matrix model for the artificialneural network 104. In addition, the tool 400 may allow the training,validation, and testing of the artificial neural network 104 to be donein parallel. Further, the tool 400 may allow for automatic stops ongradient and validation thresholds.

FIG. 5 is a flow diagram illustrating one embodiment of a method 500 topredict characteristics of an artificial heart. In one example, theartificial heart may be a magnetically levitated artificial heart. Inone embodiment, the method 500 may be implemented by the artificialneural network 104.

In one configuration, an input vector of a predetermined length may begenerated 502. A target vector of the predetermined length may also begenerated 504. In one example, the artificial neural network 104 may becreated 506 with a plurality of input neurons and a plurality of outputneurons. The created artificial neural network may be trained 508 usinga predetermined training method. For example, the input vector may beinput to the network 104 and the output of the network 104 may becompared against the target vector. One or more connections between theinput neurons may be adjusted until the output of the network 104matches the target vector with a certain degree of accuracy. In oneconfiguration, the trained artificial neural network 104 may be executedon the input vector to estimate at least one characteristic of anartificial heart. For example, the network 104 may estimate flow rates,differential pressure, hematocrit, patient orientation or somecombination of such characteristics. In one configuration, the network104 may also compute a pulsitility index. The pulsitility index may be ameasure of the variability of blood velocity in a vessel, equal to thedifference between the peak systolic and minimum diastolic velocitiesdivided by the mean velocity during a cardiac cycle.

FIG. 6 is a flow diagram illustrating one embodiment of a method 600 topredict conditions of a patient who has an artificial heart. In oneconfiguration, the method 600 may be implemented by the artificialneural network 104. To predict patient conditions, corners of the testdata may be found in non-linear space.

In one configuration, a data set of patient conditions may be generated602. The artificial neural network 104 may be trained 604 on aparticular patient condition. The network 104 may be executed 606against the data set of patient conditions. The output of the network104 for each patient condition in the data set may be compared 608 withtarget output. An error rate based on the comparison may be recorded610. In one embodiment, a determination 612 may be made as to whetheradditional patient conditions exist to use for training. If it isdetermined 612 that additional patient conditions exist, the method 600may return to train the network 104 on the additional patient conditionsincluding, for example, the execution of acts 604 through 610. If,however, it is determined 612 that additional patient conditions do notexist to use for training, at least one training set of patientconditions may be selected 614. The at least one selected set ofconditions may be selected based on an error threshold to provide aprediction across each patient condition.

In one configuration, for each patient condition C(k), the artificialneural network 104 may be trained on C(k). The network 104 may be testedagainst all C(k)'s and the errors may be recorded. In one example, theerrors may be sorted (e.g., lowest to highest). A histogram of errorbetween the trained set of conditions and the test set may indicatelinear or non-linear space between the two spaces. In one embodiment, Ntraining sets may be selected based on the desired error threshold forpredictions across all the patient conditions. In one embodiment, thepatient conditions may include, but are not limited to, combinations ofanalog left ventricle (LV) rate, LV drive pressure, venous reservoirhead, mean systemic pressure, systemic compliance, etc.

In one configuration, the patient may use a natural heart in conjunctionwith an artificial heart. The patient conditions that may be estimatedby the neural network may further include hematocrit properties of thepatient and a pulsitility index of the blood of the patient. In oneconfiguration, the pulsitility index may describe the strength of anatural heart of the patient being used in conjunction with theartificial heart. Additional conditions of the patient that may beestimated by the neural network may include blood viscosity propertiesof the patient and a recovery measurement of the natural heart of thepatient. In one embodiment, the recovery measurement may be ameasurement of the contractility of a native ventricle of the naturalheart of the patient. The recovery measurement may also be a measurementof the elastance of a native ventricle of the natural heart of thepatient.

As described above, the present systems and methods provide accurateestimates for flow, differential pressure, hematocrit, and patientorientations/conditions using an artificial neural network architecture.The present systems and methods may predict both positive and negativeflow, pump differential pressure, and viscosity (hematocrit) based onindirect signals controlling the artificial heart. The artificial heartmay include at least one magnetically levitated component. As mentionedabove, inputs may include, but are not limited to rotor speed, rotorposition, motor current, levitation current, system current, and systemvoltage.

The artificial neural network may be trained and tested with simulatedpatient conditions on a mock circulatory system. In one embodiment,training of the neural network may be performed at the “corners” of thepatient conditions simulated on the mock circulatory system. Testing maybe performed on samples of patient conditions throughout a range ofpossible patient conditions on the mock circulatory system.

FIG. 7 depicts a block diagram of one example of a computer system 710suitable for implementing the present systems and methods. Computersystem 710 includes a bus 712 which interconnects major subsystems ofcomputer system 710, such as a central processor 714, a system memory717 (typically RAM, but which may also include ROM, flash RAM, or thelike), an input/output controller 718, an external audio device, such asa speaker system 720 via an audio output interface 722, an externaldevice, such as a display screen 724 via display adapter 726, serialports 728 and 730, a keyboard 732 (interfaced with a keyboard controller733), multiple USB devices 792 (interfaced with a USB controller 790), astorage interface 734, a floppy disk drive 737 operative to receive afloppy disk 738, a host bus adapter (HBA) interface card 735A operativeto connect with a Fibre Channel network 790, a host bus adapter (HBA)interface card 735B operative to connect to a SCSI bus 739, and anoptical disk drive 740 operative to receive an optical disk 742. Alsoincluded are a mouse 746 (or other point-and-click device, coupled tobus 712 via serial port 728), a modem 747 (coupled to bus 712 via serialport 730), and a network interface 748 (coupled directly to bus 712).

Bus 712 allows data communication between central processor 714 andsystem memory 717, which may include read-only memory (ROM) or flashmemory (neither shown), and random access memory (RAM) (not shown), aspreviously noted. The RAM is generally the main memory into which theoperating system and application programs are loaded. The ROM or flashmemory can contain, among other code, the Basic Input-Output system(BIOS) which controls basic hardware operation such as the interactionwith peripheral components or devices. For example, the artificialneural network 104 to implement the present systems and methods may bestored within the system memory 717. Applications resident with computersystem 710 are generally stored on and accessed via a computer readablemedium, such as a hard disk drive (e.g., fixed disk 744), an opticaldrive (e.g., optical drive 740), a floppy disk unit 737, or otherstorage medium. Additionally, applications can be in the form ofelectronic signals modulated in accordance with the application and datacommunication technology when accessed via network modem 747 orinterface 748.

Storage interface 734, as with the other storage interfaces of computersystem 710, can connect to a standard computer readable medium forstorage and/or retrieval of information, such as a fixed disk drive 744.Fixed disk drive 744 may be a part of computer system 710 or may beseparate and accessed through other interface systems. Modem 747 mayprovide a direct connection to a remote server via a telephone link orto the Internet via an internet service provider (ISP). Networkinterface 748 may provide a direct connection to a remote server via adirect network link to the Internet via a POP (point of presence).Network interface 748 may provide such connection using wirelesstechniques, including digital cellular telephone connection, CellularDigital Packet Data (CDPD) connection, digital satellite data connectionor the like.

Many other devices or subsystems (not shown) may be connected in asimilar manner (e.g., document scanners, digital cameras and so on).Conversely, all of the devices shown in FIG. 7 need not be present topractice the present systems and methods. The devices and subsystems canbe interconnected in different ways from that shown in FIG. 7. Theoperation of a computer system such as that shown in FIG. 7 is readilyknown in the art and is not discussed in detail in this application.Code to implement the present disclosure can be stored incomputer-readable medium such as one or more of system memory 717, fixeddisk 744, optical disk 742, or floppy disk 738. The operating systemprovided on computer system 710 may be MS-DOS®, MS-WINDOWS®, OS/2®,UNIX®, Linux®, or another known operating system.

Moreover, regarding the signals described herein, those skilled in theart will recognize that a signal can be directly transmitted from afirst block to a second block, or a signal can be modified (e.g.,amplified, attenuated, delayed, latched, buffered, inverted, filtered,or otherwise modified) between the blocks. Although the signals of theabove described embodiment are characterized as transmitted from oneblock to the next, other embodiments of the present systems and methodsmay include modified signals in place of such directly transmittedsignals as long as the informational and/or functional aspect of thesignal is transmitted between blocks. To some extent, a signal input ata second block can be conceptualized as a second signal derived from afirst signal output from a first block due to physical limitations ofthe circuitry involved (e.g., there will inevitably be some attenuationand delay). Therefore, as used herein, a second signal derived from afirst signal includes the first signal or any modifications to the firstsignal, whether due to circuit limitations or due to passage throughother circuit elements which do not change the informational and/orfinal functional aspect of the first signal.

FIG. 8 is a block diagram depicting a network architecture 800 in whichclient systems 810, 820 and 830, as well as storage servers 840A and840B (any of which can be implemented using computer system 810), arecoupled to a network 850. In one embodiment, the artificial neuralnetwork 104 may be located within a server 840A, 840B to implement thepresent systems and methods. The storage server 840A is further depictedas having storage devices 860A(1)-(N) directly attached, and storageserver 840B is depicted with storage devices 860B(1)-(N) directlyattached. SAN (storage area network) fabric 870 supports access tostorage devices 880(1)-(N) by storage servers 840A and 840B, and so byclient systems 810, 820, and 830 via network 850. Intelligent storagearray 890 is also shown as an example of a specific storage deviceaccessible via SAN fabric 870.

With reference to computer system 710, modem 747, network interface 748or some other method can be used to provide connectivity from each ofclient computer systems 810, 820, and 830 to network 850. Client systems810, 820, and 830 are able to access information on storage server 840Aor 840B using, for example, a web browser or other client software (notshown). Such a client allows client systems 810, 820, and 830 to accessdata hosted by storage server 840A or 840B or one of storage devices860A(1)-(N), 860B(1)-(N), 880(1)-(N) or intelligent storage array 890.FIG. 8 depicts the use of a network such as the Internet for exchangingdata, but the present systems and methods are not limited to theInternet or any particular network-based environment.

While the foregoing disclosure sets forth various embodiments usingspecific block diagrams, flowcharts, and examples, each block diagramcomponent, flowchart step, operation, and/or component described and/orillustrated herein may be implemented, individually and/or collectively,using a wide range of hardware, software, or firmware (or anycombination thereof) configurations. In addition, any disclosure ofcomponents contained within other components should be consideredexemplary in nature since many other architectures can be implemented toachieve the same functionality.

The process parameters and sequence of acts described and/or illustratedherein are given by way of example only and can be varied as desired.For example, while the acts illustrated and/or described herein may beshown or discussed in a particular order, these acts do not necessarilyneed to be performed in the order illustrated or discussed. The variousexemplary methods described and/or illustrated herein may also omit oneor more of the acts described or illustrated herein or includeadditional acts in addition to those disclosed.

Furthermore, while various embodiments have been described and/orillustrated herein in the context of fully functional computing systems,one or more of these exemplary embodiments may be distributed as aprogram product in a variety of forms, regardless of the particular typeof computer-readable media used to actually carry out the distribution.The embodiments disclosed herein may also be implemented using softwaremodules that perform certain tasks. These software modules may includescript, batch, or other executable files that may be stored on acomputer-readable storage medium or in a computing system. In someembodiments, these software modules may configure a computing system toperform one or more of the exemplary embodiments disclosed herein.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the present systems and methods and their practicalapplications, to thereby enable others skilled in the art to bestutilize the present systems and methods and various embodiments withvarious modifications as may be suited to the particular usecontemplated.

Unless otherwise noted, the terms “a” or “an,” as used in thespecification and claims, are to be construed as meaning “at least oneof.” In addition, for ease of use, the words “including” and “having,”as used in the specification and claims, are interchangeable with andhave the same meaning as the word “comprising.”

1. A system configured to predict characteristics of an artificialheart, comprising: a processor; memory in electronic communication withthe processor; an artificial neural network configured to: receive aninput vector of a predetermined length to train the artificial neuralnetwork; produce an output vector based on the input vector; compare theoutput vector with a target vector of the predetermined length; when theoutput vector does not match the target vector within a predeterminederror rate, adjust at least one weight; when the output vector matchesthe target vector within the predetermined error rate, execute the inputvector to produce an estimate of at least one characteristic of theartificial heart.
 2. The system of claim 1, wherein the artificial heartcomprises an artificial heart having at least one magnetically levitatedcomponent.
 3. The system of claim 1, wherein the at least onecharacteristic of the artificial heart comprises positive fluid flowproduced by the artificial heart.
 4. The system of claim 1, wherein theat least one characteristic of the artificial heart comprises negativefluid flow through the artificial heart.
 5. The system of claim 1,wherein the at least one characteristic of the artificial heartcomprises differential pressure exhibited across an inlet and an outletof the artificial heart.
 6. The system of claim 1, wherein the at leastone characteristic of the artificial heart comprises hematocritproperties of the artificial heart.
 7. The system of claim 1, whereinthe artificial neural network is further configured to predict acondition of a patient that uses the artificial heart.
 8. The system ofclaim 1, wherein the input vector comprises at least one of rotor speed,rotor position, or motor current of the artificial heart.
 9. The systemof claim 1, wherein the input vector comprises at least one oflevitation current, system current, or system voltage.
 10. The system ofclaim 1, wherein the artificial heart comprises a left ventricularassist device (LVAD).
 11. The system of claim 1, wherein the artificialheart comprises a right ventricular assist device (RVAD).
 12. The systemof claim 1, wherein the at least one characteristic of the artificialheart comprises a pulsitility index.
 13. A computer-implemented methodto predict characteristics of an artificial heart using an artificialneural network, comprising: receiving, at the computer, an input vectorof a predetermined length to train the artificial neural network;producing, by the artificial neural network, an output vector based onthe input vector; comparing, by the computer, the output vector with atarget vector of the predetermined length; when the output vector doesnot match the target vector within a predetermined error rate, adjustingat least one weight of the artificial neural network; when the outputvector matches the target vector within the predetermined error rate,executing, by the artificial neural network, the input vector to producean estimate at least one characteristic of the artificial heart.
 14. Themethod of claim 13, wherein producing an estimate of at least onecharacteristic of the artificial heart comprises producing an estimateof at least one characteristic of an artificial heart that comprises atleast one magnetically levitated component.
 15. The method of claim 13,wherein the at least one characteristic of the artificial heartcomprises positive fluid flow produced by the artificial heart.
 16. Themethod of claim 13, wherein the at least one characteristic of theartificial heart comprises negative fluid flow through the artificialheart.
 17. The method of claim 13, wherein the at least onecharacteristic of the artificial heart comprises differential pressureexhibited across an inlet and an outlet of the artificial heart.
 18. Themethod of claim 13, wherein the at least one characteristic of theartificial heart comprises hematocrit properties of the artificialheart.
 19. The method of claim 13, further comprising using theartificial neural network to predict a condition of a patient that usesthe artificial heart.
 20. The method of claim 13, wherein the inputvector comprises at least one of rotor speed, rotor position, or motorcurrent of the artificial heart.
 21. The method of claim 13, wherein theinput vector comprises at least one of levitation current, systemcurrent, or system voltage.
 22. The method of claim 13, wherein the atleast one characteristic of the artificial heart comprises a pulsitilityindex.
 23. A computer-program product for predicting characteristics ofan artificial heart using an artificial neural network, thecomputer-program product comprising a non-transitory computer-readablemedium having instructions thereon, the instructions comprising: codeprogrammed to receive an input vector of a predetermined length to trainthe artificial neural network; code programmed to produce an outputvector based on the input vector; code programmed to compare the outputvector with a target vector of the predetermined length; when the outputvector does not match the target vector within a predetermined errorrate, code programmed to adjust at least one weight; when the outputvector matches the target vector within the predetermined error rate,code programmed to execute the input vector to produce an estimate atleast one characteristic of the artificial heart.
 24. Thecomputer-program product of claim 23, wherein the artificial heartcomprises an artificial heart having at least one magnetically levitatedcomponent.
 25. The computer-program product of claim 23, wherein theartificial heart comprises a left ventricular assist device (LVAD). 26.The computer-program product of claim 23, wherein the artificial heartcomprises a right ventricular assist device (RVAD).
 27. A systemconfigured to predict conditions of a patient using an artificial heart,comprising: a processor; memory in electronic communication with theprocessor; an artificial neural network configured to: receive an inputvector of a predetermined length to train the artificial neural network;produce an output vector based on the input vector; compare the outputvector with a target vector of the predetermined length; when the outputvector does not match the target vector within a predetermined errorrate, adjust at least one weight; when the output vector matches thetarget vector within the predetermined error rate, execute the inputvector to produce an estimate of at least one condition of the patientusing the artificial heart.
 28. The system of claim 27, wherein thepatient uses a natural heart in conjunction with the artificial heart.29. The system of claim 27, wherein the at least one condition of thepatient comprises hematocrit properties of the patient.
 30. The systemof claim 27, wherein the at least one condition of the patient comprisesa pulsitility index of the blood of the patient.
 31. The system of claim30, wherein the pulsitility index describes the strength of a naturalheart of the patient being used in conjunction with the artificialheart.
 32. The system of claim 27, wherein the at least one condition ofthe patient comprises blood viscosity properties of the patient.
 33. Thesystem of claim 27, wherein the at least one condition of the patientcomprises a recovery measurement of the natural heart of the patient.34. The system of claim 33, wherein the recovery measurement comprises ameasurement of the contractility of a native ventricle of the naturalheart of the patient.
 35. The system of claim 33, wherein the recoverymeasurement comprises a measurement of the elastance of a nativeventricle of the natural heart of the patient.